Predicting the binding preference of transcription factors to individual DNA k-mers.
about
Affinity regression predicts the recognition code of nucleic acid–binding proteinsUsing topology to tame the complex biochemistry of genetic networksComputational identification of diverse mechanisms underlying transcription factor-DNA occupancyStatistical tests for natural selection on regulatory regions based on the strength of transcription factor binding sites.Predicting DNA-binding specificities of eukaryotic transcription factorsExploring the DNA-recognition potential of homeodomainsIncreasing coverage of transcription factor position weight matrices through domain-level homology.An improved SELEX-Seq strategy for characterizing DNA-binding specificity of transcription factor: NF-κB as an example.Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge.Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functionsDetermination and inference of eukaryotic transcription factor sequence specificity.Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels.Recognition models to predict DNA-binding specificities of homeodomain proteins.An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.Past Roadblocks and New Opportunities in Transcription Factor Network Mapping.Integrated analysis of motif activity and gene expression changes of transcription factors.Correlated evolution of transcription factors and their binding sites.
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P2860
Predicting the binding preference of transcription factors to individual DNA k-mers.
description
article científic
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article scientifique
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articolo scientifico
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artigo científico
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bilimsel makale
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scientific article published on 16 December 2008
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
Predicting the binding preference of transcription factors to individual DNA k-mers.
@en
Predicting the binding preference of transcription factors to individual DNA k-mers.
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type
label
Predicting the binding preference of transcription factors to individual DNA k-mers.
@en
Predicting the binding preference of transcription factors to individual DNA k-mers.
@nl
prefLabel
Predicting the binding preference of transcription factors to individual DNA k-mers.
@en
Predicting the binding preference of transcription factors to individual DNA k-mers.
@nl
P2093
P2860
P356
P1433
P1476
Predicting the binding preference of transcription factors to individual DNA k-mers
@en
P2093
Andrew R Gehrke
Anthony A Philippakis
Gwenael Badis
Martha L Bulyk
Michael F Berger
Shaheynoor Talukder
Timothy R Hughes
Trevis M Alleyne
P2860
P304
P356
10.1093/BIOINFORMATICS/BTN645
P407
P577
2008-12-16T00:00:00Z